HYPERSPECTRAL TREE SPECIES CLASSIFICATION WITH AN AID OF LIDAR DATA
Tomohiro Matsuki1, Naoto Yokoya2, Akira Iwasaki2
12Department of Aeronautics and Astronautics, The University of Tokyo
Research Center for Advanced Science and Technology, The University of Tokyo
ABSTRACTClassification of tree species is one of the most important
applications in remote sensing. A methodology to classifytree species using hyperspectral and LiDAR data isproposed. The data processing consists of shadow
correction, individual tree crown delineation, classification
by support vector machine (SVM) and postprocessing by asmoothing filter. The authors applied this procedure to thedata taken over Tama Forest Science Garden in Tokyo,
Japan and classified it into 16 classes of tree species. As aresult, the authors achieved classification accuracy of 79 %with 10 % training data, which is 17 % higher than what is
obtained by using hyperspectral data only. Shadow
correction and morphological processing derived fromLiDAR data increase the accuracy by 3 % and 14 %,respectively.

(b) Canopy height model(a) RGB image
Fig. 1. Study area

Index Terms— classification, forest, hyperspectral data,LiDAR, data fusion
1. INTRODUCTION
Tree species classification is an important issue on the studyof land cover or forest management. Periodic field surveys
by experts cost a large amount of time and human resources.
Since remote sensing can provide wide area observation at
one time, its contribution to tree species classification isexpected. During the past years, many researches haveworked on classification or identification of tree species.
Since hyperspectral sensors have hundreds of observation
bands and high spectral resolution, we can obtain acontinuous spectrum that enables more detailed analysis. Inaddition to spectral data, light detection and ranging(LiDAR) data is very informative to obtain canopy heightmodels (CHMs). In recent years, fusion of these two datafor classification has been studied [1]. Many researchers
used LiDAR data as preprocessing or raw height data.
Morphological information, such as tree shape, obtained
from LiDAR-derived CHMs is expected to be more useful.
In this work, we propose a methodology to classify tree
species by coupling hyperspectral and LiDAR data. In themethodology, we extract spectral and morphologicalinformation and input them to its classifiers. We apply theprocedure to a challenging tree species classification
problem and show its effectiveness.

Fig. 2. Ground truth
2. STUDY AREA
The study area is Tama Forest Science Garden in Tokyo,
Japan. Fig.1 shows the RGB image derived fromhyperspectral data and the CHM derived from LiDAR data.
The CHM is the difference of the digital elevation model(DEM) and the digital surface model (DSM), which
represents the height of trees excluding terrain effects.
These data are taken by airborne sensors on 10 September,2009 and provided by Japan Space Systems. The groundsampling distance of the both data is 1 m. The observation
wavelength of the hyperspectral sensor ranges 400 – 1050
nm with 72 bands. Field survey has been carried out in thisarea tree by tree. Fig. 2 shows the distribution of the groundtruth. The species and the crown shapes of some trees areinvestigated by experts. There are more than 90 species oftrees in this area. However, most of species occupy few
pixels. We choose major 16 species that have enoughground truth data. Tab. 1 shows the class names and thenumber of samples. This is the typical mixed forest ofconifers and broad leaved trees.

Tab. 1. Major class names
123456789101112131415
16

English name
California incense
cedar
Bald cypress
Japanese cypress
Japanese ceder
Deodar cedar
Loblolly pine
Eastern whitepine
Koyama's spruce
Momi firAmerican
sweetgum
Japanese bigleaf
magnolia
Painted maple
Oriental raisintree
Chinese evergreen
oakJapanese blue oak
A species of oak

Scientific nameSamples

Calocedrus decurrens

304

Taxodium distichum
Chamaecyparis obtusa
Cryptomeria japonica
Cedrus deodaraPinus taeda

9278753327833
332

Pinus strobus

579

Picea koyamae
Abies firma

267
282

Liquidambar styraciflua

733

Magnolia obovata

380

Acer pictum

403

Hovenia dulcis

361

Quercus myrsinifolia

948

Quercus glauca
Quercus serrata

1083
987

3. METHODOLOGY
The proposed method consists of four parts: (1) shadow
correction for hyperspectral data, (2) individual tree crowndelineation from LiDAR-derived CHMs, (3) classification
by support vector machine (SVM), and (4) postprocessing.
Fig. 3 shows the classification flow.

Fig. 3. Classification flow(a) De-shadowed RGB(b) Individual tree crownimagedelineation
Fig.4. Preprocessed image.

3.1. Shadow correction
There are many shadows in forest, which affectclassification results. Therefore, shadows in hyperspectral
data need to be modified for accurate classification. We
used the unmixing-based approach for de-shadowing ofreflectance data [2]. First, the shadow is defined as a
“black” (zero reflectance) endmember. Next, endmember
spectra, which are thought to be non-shadowed, are selectedby vertex component analysis (VCA). Abundance fractions
are estimated using the fully constrained least squares(FCLS) method satisfying sum-to-one and non-negativity
constraints. The reflectance spectra are approximately deshadowed by dividing the spectra by (1 – shadow
abundance fraction). Fig. 4 (a) shows the de-shadowed
image of the study area.

for LiDAR derived CHMs [4]. First, to reduce noise, weapply a Gaussian smoothing filter with the kernel size of 3
pixels to the CHM, which is determined not to miss smalltrees. Then, we find local maxima of the smoothed image.
They include non-tree objects. Thus the local maxima
whose normalized difference vegetation indices (NDVI) arelower than 0.5 or heights are lower than 1 m are excluded.
The rest of them correspond to the tops of trees. Regions
corresponding tree crowns grow from the local maxima. If
the neighboring pixels satisfy some conditions, regions
grow and the neighboring pixels become the next starting
pixels. The conditions are set that the height of theneighboring pixel is lower than that of the starting pixel andhigher than 1 m so that regions do not expand to the ground.
This growing step is repeated until there is no starting pixel.
After the growing step, each region is adjusted to be starshaped, by which we exclude the pixels that locate out of
the region centered by treetop [4]. Finally, overlapped
regions are assigned to the region of the nearest local
maxima. Fig. 4 (b) shows the result of the delineation. The
red dots represent the tops of trees.

3.2. Individual tree crown delineation
Individual tree crown delineation is one of the important
techniques to extract information of forest. Many
researchers have investigated individual tree crowndelineation methods [3]. We used a region growing method3.3. Classification by SVM
We classify data using SVM with the radial basis function(RBF) kernel to deal nonlinear distribution. The input data
are spectral information and morphological information.
The dimension of the hyperspectral data is 72 but the latent

(a) Hyperspectral-only(b) Proposed method

Fig. 6. Class accuracy(b) Ground truth
Fig. 5. Classification map
dimension is lower. Therefore, the spectral information isprincipal components of the modified hyperspectral data.
We input 15 principal components that represent more than
99.5 % of the whole data. Then, the canopy height, size andcurvature are used for the morphological information. The
height and the size are defined by the highest value of CHM
and the number of pixels in each crown, respectively. The
curvature is estimated by fitting function defined as, where we assume axial symmetry and use
cylindrical coordinates (r, z). H denotes the height of thetree, and a and c are estimated values. If a pixel is notincluded in any crowns, the height is its own value, the sizeis 1 and the curvature is 0. The bandwidth is decided bycross-validation. We coded this method in MATLAB withLIBSVM, which is the library for support vector machine[5]. It supports multi-class classification using one-againstone method.
3.4. Postprocessing
Since the spectra are different depending on the pixels evenin the same crown, the classification map becomes noisy.
We apply a crown-preserving smoothing filter as
postprocessing. The filter is based on a Gaussian filter andweights depending on crowns segment. If filtered pixels
belong to the same crown, the weight is 1. Otherwise, theweight is smaller than 1. Since the classification results are
categorical values, we re-classify the data to the class inwhich score is highest after smoothing.
4. RESULT
We applied our method to the hyperspectral and LiDARderived CHM taken over forest areas. The training set,
whose size is given as a percentage to all ground truth, wasrandomly selected and commonly used for comparison
methods in one test. Fig. 5 shows the classification map
when the training set size is 10 %. Here, the classification
accuracy is evaluated only in the regions with ground truth.
The accuracy of the proposed method is better than thatobtained by spectral information only. Since the proposed
method uses morphological information, we can use
information on crown segment, and thus less noisy result isachieved. Furthermore, the proposed method improves theresult of the central area and Koyama’s spruce in thenortheast area. Fig. 6 shows the accuracy of each class,which is obtained by averaging the result of multiple tests.
The accuracy of the proposed method is higher thanhyperspectral-only method in most of the classes. The
overall accuracy is shown in Fig. 7, in which the training setsize was changed from 1 % to 30 %. It is compared withthree methods, i.e. original (non-modified) spectral
information only, shadow corrected spectral information
only, and original spectral and morphological information.
It is found that shadow correction increases the accuracy by
3 % and morphological information increases it by 14 %.
Fig. 8 shows the results of postprocessing including theregion where ground truth data do not exist. It is comparedwith majority voting in each crown. The proposed filter cansmooth results preserving smaller structures than majority
voting. Moreover, applying the proposed filter multiple
times can smooth results strongly preserving crown edges.
Fig. 9 shows the overall accuracy with postprocessing.
Majority voting makes the results worse on average. In
contrast, the proposed filter improves the results. Applying
the smoothing filter multiple times increases the accuracy.
These results depend on the delineation but the proposed
filter can preserve small structures even if the delineation isincomplete. This work shows that hyperspectral data withmorphological information is effective for tree speciesclassification in Japanese forests, in which conifers andbroad leaved trees make complex biodiversity.

Fig. 7. Overall accuracy

Fig. 9. Overall accuracy (postprocessing)
ACKNOWLEDGEMENT
Hyperspectral data and LiDAR data is provided by Japan
Space Systems. This work is carried out under the contract
with National Institute of Advanced Industrial Science andTechnology for the Hyperspectral Imager Suite (HISUI)
project.

(a) Before(Accuracy 79.53 %)(b) Majority voting(Accuracy 79.19 %)(d) Proposed filter (multiple)(c) Proposed filter (once)(Accuracy 81.97 %)(Accuracy 80.47 %)Fig. 8. Classification map (postprocessing)
5. CONCLUSION
We proposed the methodology to classify tree species using
hyperspectral data and LiDAR-derived CHMs. We applied
it to the data taken over Tama Forest Science Garden inTokyo, Japan and classified the data into 16 classes. The
proposed method shows higher performance than themethod using hyperspectral data only. It is found thatshadow correction and morphological information areuseful for tree species classification, showing theimportance of height data. Furthermore, the crownpreserving smoothing filter as postprocessing improves theclassification results.

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